{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.建模准备"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.1.导入模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import math\n",
    "import warnings\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import auc\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.2.设置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "warnings.filterwarnings('ignore')\n",
    "np.set_printoptions(suppress=True)\n",
    "pd.set_option('display.width', 180)\n",
    "pd.set_option('display.max_rows', None)\n",
    "pd.set_option('display.max_columns', 100)\n",
    "pd.set_option('display.float_format', lambda x: '%.6f' % x)\n",
    "plt.rcParams['font.sans-serif'] = [\"SimHei\"]\n",
    "plt.rcParams[\"axes.unicode_minus\"] = False\n",
    "plt.rcParams['font.size'] = '12'\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.数据准备"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.1.读取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.read_csv(r'D:/LKM/信用评分卡模型/data/training.csv')\n",
    "df_test = pd.read_csv(r'D:/LKM/信用评分卡模型/data/test.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.2.查看数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.766127</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>0.802982</td>\n",
       "      <td>9120.0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.957151</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0.121876</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.658180</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0.085113</td>\n",
       "      <td>3042.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.233810</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>0.036050</td>\n",
       "      <td>3300.0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.907239</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>0.024926</td>\n",
       "      <td>63588.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines  age  NumberOfTime30-59DaysPastDueNotWorse  DebtRatio  MonthlyIncome  NumberOfOpenCreditLinesAndLoans  \\\n",
       "0           1                 1                              0.766127   45                                     2   0.802982         9120.0                               13   \n",
       "1           2                 0                              0.957151   40                                     0   0.121876         2600.0                                4   \n",
       "2           3                 0                              0.658180   38                                     1   0.085113         3042.0                                2   \n",
       "3           4                 0                              0.233810   30                                     0   0.036050         3300.0                                5   \n",
       "4           5                 0                              0.907239   49                                     1   0.024926        63588.0                                7   \n",
       "\n",
       "   NumberOfTimes90DaysLate  NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  NumberOfDependents  \n",
       "0                        0                             6                                     0                 2.0  \n",
       "1                        0                             0                                     0                 1.0  \n",
       "2                        1                             0                                     0                 0.0  \n",
       "3                        0                             0                                     0                 0.0  \n",
       "4                        0                             1                                     0                 0.0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.3.数据摘要"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150000 entries, 0 to 149999\n",
      "Data columns (total 12 columns):\n",
      " #   Column                                Non-Null Count   Dtype  \n",
      "---  ------                                --------------   -----  \n",
      " 0   Unnamed: 0                            150000 non-null  int64  \n",
      " 1   SeriousDlqin2yrs                      150000 non-null  int64  \n",
      " 2   RevolvingUtilizationOfUnsecuredLines  150000 non-null  float64\n",
      " 3   age                                   150000 non-null  int64  \n",
      " 4   NumberOfTime30-59DaysPastDueNotWorse  150000 non-null  int64  \n",
      " 5   DebtRatio                             150000 non-null  float64\n",
      " 6   MonthlyIncome                         120269 non-null  float64\n",
      " 7   NumberOfOpenCreditLinesAndLoans       150000 non-null  int64  \n",
      " 8   NumberOfTimes90DaysLate               150000 non-null  int64  \n",
      " 9   NumberRealEstateLoansOrLines          150000 non-null  int64  \n",
      " 10  NumberOfTime60-89DaysPastDueNotWorse  150000 non-null  int64  \n",
      " 11  NumberOfDependents                    146076 non-null  float64\n",
      "dtypes: float64(4), int64(8)\n",
      "memory usage: 13.7 MB\n"
     ]
    }
   ],
   "source": [
    "df_train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.4.描述性统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>1.202690e+05</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>146076.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>75000.500000</td>\n",
       "      <td>0.066840</td>\n",
       "      <td>6.048438</td>\n",
       "      <td>52.295207</td>\n",
       "      <td>0.421033</td>\n",
       "      <td>353.005076</td>\n",
       "      <td>6.670221e+03</td>\n",
       "      <td>8.452760</td>\n",
       "      <td>0.265973</td>\n",
       "      <td>1.018240</td>\n",
       "      <td>0.240387</td>\n",
       "      <td>0.757222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>43301.414527</td>\n",
       "      <td>0.249746</td>\n",
       "      <td>249.755371</td>\n",
       "      <td>14.771866</td>\n",
       "      <td>4.192781</td>\n",
       "      <td>2037.818523</td>\n",
       "      <td>1.438467e+04</td>\n",
       "      <td>5.145951</td>\n",
       "      <td>4.169304</td>\n",
       "      <td>1.129771</td>\n",
       "      <td>4.155179</td>\n",
       "      <td>1.115086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>37500.750000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.029867</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.175074</td>\n",
       "      <td>3.400000e+03</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>75000.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.154181</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.366508</td>\n",
       "      <td>5.400000e+03</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>112500.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.559046</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.868254</td>\n",
       "      <td>8.249000e+03</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>150000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>50708.000000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>329664.000000</td>\n",
       "      <td>3.008750e+06</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Unnamed: 0  SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines            age  NumberOfTime30-59DaysPastDueNotWorse      DebtRatio  MonthlyIncome  \\\n",
       "count  150000.000000     150000.000000                         150000.000000  150000.000000                         150000.000000  150000.000000   1.202690e+05   \n",
       "mean    75000.500000          0.066840                              6.048438      52.295207                              0.421033     353.005076   6.670221e+03   \n",
       "std     43301.414527          0.249746                            249.755371      14.771866                              4.192781    2037.818523   1.438467e+04   \n",
       "min         1.000000          0.000000                              0.000000       0.000000                              0.000000       0.000000   0.000000e+00   \n",
       "25%     37500.750000          0.000000                              0.029867      41.000000                              0.000000       0.175074   3.400000e+03   \n",
       "50%     75000.500000          0.000000                              0.154181      52.000000                              0.000000       0.366508   5.400000e+03   \n",
       "75%    112500.250000          0.000000                              0.559046      63.000000                              0.000000       0.868254   8.249000e+03   \n",
       "max    150000.000000          1.000000                          50708.000000     109.000000                             98.000000  329664.000000   3.008750e+06   \n",
       "\n",
       "       NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  NumberOfDependents  \n",
       "count                    150000.000000            150000.000000                 150000.000000                         150000.000000       146076.000000  \n",
       "mean                          8.452760                 0.265973                      1.018240                              0.240387            0.757222  \n",
       "std                           5.145951                 4.169304                      1.129771                              4.155179            1.115086  \n",
       "min                           0.000000                 0.000000                      0.000000                              0.000000            0.000000  \n",
       "25%                           5.000000                 0.000000                      0.000000                              0.000000            0.000000  \n",
       "50%                           8.000000                 0.000000                      1.000000                              0.000000            0.000000  \n",
       "75%                          11.000000                 0.000000                      2.000000                              0.000000            1.000000  \n",
       "max                          58.000000                98.000000                     54.000000                             98.000000           20.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.5.缺失值统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Column</th>\n",
       "      <th>Number of Null Values</th>\n",
       "      <th>Proportion</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Unnamed: 0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SeriousDlqin2yrs</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>RevolvingUtilizationOfUnsecuredLines</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>age</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NumberOfTime30-59DaysPastDueNotWorse</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>DebtRatio</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>MonthlyIncome</td>\n",
       "      <td>29731</td>\n",
       "      <td>0.198207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NumberOfOpenCreditLinesAndLoans</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NumberOfTimes90DaysLate</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NumberRealEstateLoansOrLines</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NumberOfTime60-89DaysPastDueNotWorse</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NumberOfDependents</td>\n",
       "      <td>3924</td>\n",
       "      <td>0.026160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  Column  Number of Null Values  Proportion\n",
       "0                             Unnamed: 0                      0    0.000000\n",
       "1                       SeriousDlqin2yrs                      0    0.000000\n",
       "2   RevolvingUtilizationOfUnsecuredLines                      0    0.000000\n",
       "3                                    age                      0    0.000000\n",
       "4   NumberOfTime30-59DaysPastDueNotWorse                      0    0.000000\n",
       "5                              DebtRatio                      0    0.000000\n",
       "6                          MonthlyIncome                  29731    0.198207\n",
       "7        NumberOfOpenCreditLinesAndLoans                      0    0.000000\n",
       "8                NumberOfTimes90DaysLate                      0    0.000000\n",
       "9           NumberRealEstateLoansOrLines                      0    0.000000\n",
       "10  NumberOfTime60-89DaysPastDueNotWorse                      0    0.000000\n",
       "11                    NumberOfDependents                   3924    0.026160"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "null_val_sums = df_train.isnull().sum()\n",
    "pd.DataFrame({\"Column\": null_val_sums.index, \n",
    "              \"Number of Null Values\": null_val_sums.values,\n",
    "              \"Proportion\": null_val_sums.values / len(df_train) })"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.EDA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.1.目标：SeriousDlqin2yrs（出现90天或更长时间的逾期行为）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='SeriousDlqin2yrs', ylabel='count'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, axe = plt.subplots(1,2,figsize=(12,4))\n",
    "df_train.SeriousDlqin2yrs.value_counts().plot(kind='pie', autopct='%.2f%%', shadow=True, explode=[0,0.1], ax=axe[0],)\n",
    "sns.countplot('SeriousDlqin2yrs', data=df_train, ax=axe[1],)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.2.特征：RevolvingUtilizationOfUnsecuredLines（信用卡和个人可用额度的总和与信用限额之比）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "\n",
    "ax = plt.subplot(2,2,1)\n",
    "sns.distplot(df_train.RevolvingUtilizationOfUnsecuredLines, fit=stats.norm)\n",
    "ax = plt.subplot(2,2,2)\n",
    "res = stats.probplot(df_train.RevolvingUtilizationOfUnsecuredLines, plot=plt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.3.特征：age（年龄）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "\n",
    "ax = plt.subplot(2,2,1)\n",
    "sns.distplot(df_train.age, fit=stats.norm)\n",
    "ax = plt.subplot(2,2,2)\n",
    "res = stats.probplot(df_train.age, plot=plt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.4.特征：NumberOfTime30-59DaysPastDueNotWorse（近两年内出现逾期35-59天的次数）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "\n",
    "ax = plt.subplot(2,2,1)\n",
    "sns.distplot(df_train['NumberOfTime30-59DaysPastDueNotWorse'], fit=stats.norm)\n",
    "ax = plt.subplot(2,2,2)\n",
    "res = stats.probplot(df_train['NumberOfTime30-59DaysPastDueNotWorse'], plot=plt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.5.特征：DebtRatio（每月偿还债务，赡养费，生活费用除以当月总收入）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0ZtYYXsJOK9WSJEkDY/Pm+bUvtHaS6naWn+32UrZz9svM9Zm5KjNXLV++vI3TmFmj0bBSLUmSNEBWrJhf+0JrJ6luZ/nZbi9l29MlbxvDS0yqJUmSBsi6dTAysnvbyEjRXoV2ptS7lt2Xnz0zIi7MzPNn6dPpUrat9rdgGsNLmHjMeaolSZIGxeho8Tg2Vgz5WLGiSKgn23ttzqQ6M7dGxBrgZOCPM/Nu4Ltz9HkQoN22ch9r5trfQmkMDzv7h6S+FxHHz/RaZt7Yy1gkqR+MjlaXRE/X1uIv7Sw/2+2lbHu55G1jeAk7dmzvxaEkqRMnlI8vAHYAG4BjgP2A51cUkyQJV1QEYHh4iZVqSX0vMy8AiIgvZeaLJtsj4svVRSVJApNqYHL4h2OqJQ2MnRHxuxRD8Z5RdTCSpDaXKV/sGkusVEsaKKcDTwDOBPYvn0uSKmSlGmg0llipljQwMvOBiPg88GRgM/CzikOSpNqzUg0MLzGpljQ4IuLDwAXARcBTgY9XG5EkyaQaGGoMs8OkWtLgODIzXwk8kJmfAw6oOiBJqjuTaiYr1Y6pljQw7o2IPwKWRcRZwN1VByRJdWdSjWOqJQ2c1wEPAjdTVKnfUG04kiS/qEg5+4fLlEsaEJn5KPChquOQJE2xUk05T/WEwz8kDYaIuK7qGCRJuzOpplhRcYeVakmD47aIeFnVQUiSpjj8AxhyRUVJg+VXgbdFxG3AI0Bm5okVxyRJtWZSTVGpntjxGJlJRFQdjiTNKjNPqDoGSdLuTKqBxvASAHZOTNAY9pJI6n8R8UymVlS8MzMfrjgkSao1x1QzlVQ7BETSIHBFRUnqPybVsKs6bVItaUC4oqKkWhsfh5UrYWioeBwfrzoih38AxZhqwKXKJQ0KV1SUVFvj47B2LWzbVjzftKl4DjA6Wl1cVqppHv7hXNWSBoIrKkqqrXPPnUqoJ23bBmNj1cQzyUo1jqmWNBgi4vimp/9c/gCsAm7sfUSS1Fvj43D//a1f27y5t7FMZ1JN85hqK9WS+trkVHovAB4DNgLHAPsBz68oJknqmXPPnfm1FSt6F0crDv8AGkusVEvqf5l5QWZeUGzmizPz3Zl5KkWCPauIOCQibmp6fllE3BwR5y9UmyR102xVaoB163oXSysm1UCj4ewfkgbKzoj43Yh4QUS8Za7OEbEMuBLYp3x+GtDIzOOAp0bE4d1uW5jTllRns42ZPuigar+kCCbVAAyXleodj5lUSxoIpwNPAM6k+KLi6XP0nwDOALaWz9cAV5fb1wPPW4C23UTE2ojYEBEb7r333rnOT5IeZ7Yx0x/6UO/imIlJNU1fVJxwTLWk/peZD2TmH2fmOZl5UWbO8gdRyMytmflgU9M+wF3l9k+AQxagbXoM6zNzVWauWr58efsnK0mlmcZM90OVGkyqgaak2kq1pAEQEdd1uIuHgaXl9r4U/xZ0u02SumrdOhgZ2b1tZKQ/qtTgjQ9wSj1JA+e2iHhZB+/fyNQQjaOBOxagTZK6anQU1q+Hww6DiOJx/fr+qFKDU+oBU1PquaKipAHxq8DbIuI24BGK2UBOnMf7rwVuiohDgVOA1UB2uU2Sum50tH+S6OmsVDO1TPlO56mWNAAy84TMXJqZzy6320qoM3NN+biV4suFtwAnZOaD3W7r2slK0oCwUs3U8A8r1ZL6WUQMAa+mqFTvTzGbxzeBazJz53z2lZlbmJqxY0HaJKlOTKpx8RdJA+Myiqn0vgZso5h14zTgJcAbKoxLkmqvraQ6Ii4DjgA+l5kXtttnT9siYhj49/IH4G2ZedsenuOcXPxF0oD4xcw8flrbn0XE1yqJRpK0y5xJdfNKWRFxeUQcnpk/mKsPcGQHbfsBn8jM87p9wq24+IukAXFfRFwM3EjxBcV9KcYy31NlUJKk9irVa3j8Slk/aKPPszpoWwq8NCJOAG4Dfjszd/sWYUSsBdYCrJhpNvA2DU1Wql38RVJ/ey3wNuBNFMWHrcA3gPdWGJMkifaS6ukrZR3bZp9O2r4EnJSZ/xURHwNOBT7TfMDMXA+sB1i1alW2cR4ziggaw0tc/EVSX8vMR4CLq45DkvR47STV7ayU1e2VuG7NzJ+VbRuAw9uIsyON4WEr1ZIkSdoj7cxT3c5KWd1eieuqiDg6IhrAy4HvthFnR6xUS5Ik9afxcTj44GIlxYhie3y86qh2106l+lp2XynrzIi4MDPPn6VPpytx3Qp8HAjgM5n5xU5PdC6N4WFn/5AkSeozb3kLXHrp7m333w+/9VvFdr+ssDhnpbrFSlnfnZZQL8TqXLdn5lGZeWRmjnXnVGc3PLzExV8kSZL6yPg4fOQjrV/bvh3GepIltqetearbWSlr0FfiGhpewoTLlEuSJPWNsTHIWaaj2Ly5d7HMpZ0x1bUwPLzE4R+SJEl9ZK6kucNZlbvKpLpUjKm2Ui1JktQv9tln5tf22gvWretdLHMxqS41hpcwsWN71WFIkiSJYjz1ww+3fi0CLr+8f76kCCbVu1ipliRJ6h/nnjv76/2UUINJ9S7O/iFJktQfxseLafNm0k9jqSeZVJec/UOSJKk/vPnNM78W0V9jqSeZVJec/UOSJKl6J50081hqKBLufhv6AW3OU10HrqgoSZJUrWc8A77//Zlfj4BLLuldPPNhpbrUWGKlWpIkqSpzJdQw+0IwVTOpLg0PL2HHYybVkiRJvTY+PndCDXDYYQsfy54yqS41/KKiJElSJc46a+4+w8P9+QXFSSbVpeG99uax7T8l+/nvCpIkSYvI+HgxTnpiYu6+V1zRn19QnOQXFUsj++7PzokJtv90W9WhSJIkLXonnQRf+lJ7ff/qr/o7oQaT6l1G9jsAgG0Pba04EkmSpMWrnS8kNhuEhBoc/rHLyL77A7DtoQcrjkSSJGnxectbiqEe80mozzlnMBJqsFK9y1Sl2qRakiSpG8bH4TWv2bP3Hnpo/85J3YpJdWnpfmWl+mGHf0iSJO2JiO7sZ+lSuOuu7uyrV0yqSyP7WqmWJEmaS7cS55ksXQrbBnDeCJPq0t5LRxhqDFupliRJtbPQiXK7nvhE2LKl6ij2jF9ULEUEI/vtz6NWqiVJ0oCI6M5PP3jhCwc3oQaT6t2M7HuAlWpJktRV3Up8+zUZ7tQ550AmfPGLVUfSGYd/NBnZb3/HVEtSnxkfh7Ex2LwZVqwolikeHX18+6mnwuc/D5s2QaNRrNAWUfxjLan/HHpoG19G3Llz6mdiorvb++8PT396187HpLrJ0n335547f1R1GJI0kCLiMuAI4HOZeWE39jk+DmvXTn1padOm4vnXvw5XXrl7+6WXTr1vcsljE2pNSRpMMMROhti5qLb7JY52tw89pExufzoBy+ZIgBfSS14C113Xtd2ZVDcZ2c/hH5K0JyLiNKCRmcdFxOURcXhm/qDT/Y6NPX4WgG3bYP36qcS597LypGSxbPfqeA0WODnrAxMMlWdanPlCbj/Gkrb7/+boEAwNFX8+Gqp4e/rzQw7p6u/ApLrJyL778+gjDzExMUGj0ag6HEkaJGuAq8vt64HnAbuS6ohYC6wFWLFiRds73by5eHwV1/BarppKmCaqSQCHqSyT75luJmdz9dvBMNvZq2fJ4GLYbvUaVD+42r8KmVTvZmS/AyCTrVu3smzZsqrDkaRBsg8wOTryJ8CxzS9m5npgPcCqVava/ud3xYpiaMcBPMhT+I+mxGIq3V2I5KxfEqheb/dDcqb+YaI8PybVTSaXKt+yZYtJtSTNz8PA0nJ7X7o0u9S6dcUY6su2nc1lnA3AyAicddbuY6olTTEZroZT6jUZ2bdYqnzLIE+SKEnV2Egx5APgaOCObux0dLQYP33YYcVMHocdVjy/5JLHt59zTvEIxbBJWDxTjmnxyVy4H1XDSnWTpWWl+p577qk4EkkaONcCN0XEocApwOpu7Xh0tPhpt12SqmClurQzkzsf24+99z2A8Y9/gvR/9SSpbZm5leLLircAJ2Smk/5LqpW2kuqIuCwibo6I8+fTp9tt3fTo9gl+eM/DPLp9gq0/fYyPfv1HXHvbPTy0cg0bN3yL09/zl3zs5jv4/n9u5b6Hf8bOnSbZkjSbzNySmVdn5t1VxyJJvTbn8I925h5t1Qc4sptt3ZjvtNmt//EAZ6y/ZdfzJY3gFc96Mgc99ze5+sKv8b1rPsDtf/shiHaL+THLs+ldHeQnLUZ/cO7vcPbZZ1cdhiSpAjHXMIeI+L/AP2Tm5yPiTGBpZn50rj7As7rZ1uKYu+Y8BZ4O/OseXoODgfv28L29MggxgnF2m3F2T7/HeFhmLq86iF6JiHuBTT08ZL///hdKXc8b6nvunndvtLxnt/NFxVnnHp2lT7fbdtM852knImJDZq7qdD8LaRBiBOPsNuPsnkGIsU56/T8Qdf391/W8ob7n7nlXq52xDe3MPdqqT7fbJEmSpL7UTrLaztyjrfp0u02SJEnqS+0M/7iW3ecePTMiLszM82fpsxrILrctlI6HkPTAIMQIxtltxtk9gxCjFk5df/91PW+o77l73hWa84uKABGxDDgZuHGmqZJa9el2myRJktSP2kqqJUmSJM3MLwBKGigRcWBEnBwRB1cdiwZHRBwSEf9cdRy9EhEHRMR1EXF9RHw6IvaqOiYtnLr/vvvl813bpHqhV2yc49jDEbE5Im4of47st9Uny/9Ab+pGLAsZc3Ocra5r1XG2utH147WcIc6+upblPpYBnwWeDXwlIpb34/VUX/oTpmaVqoNR4AOZ+SLgbuAlFcfTEzX+/Nby992kLz7ftUyqo2kFSOCpUazi2EtHAZ/IzDWZuQY4fHo8rWJst63T4MrE5UqK+cJbXq9ut3UjTqZd18y8rQ/inH6jO7Mfr2WLON/Zh9cSit/x2zNzHfCPwIndjKkP7g1aABFxIvAIxX/btZCZl2TmF8qny4F7qoynF+r8+a3j73tSP32+25n9YzFaA1xdbl9PMX1fV5dBn8Nq4KURcQJwG/CzFvE8q4O2Ts9lAjgD+Lvy+Zoux9etmKfHOf26/vYCxD6vODPzkqany4HXAB/sYjxduZYt4ryTPruWZZxfBYiI4ymq1Qd2OaaF+DyphyLizylW2Z30ZeAE4BUUM1UtSq3OOzPfFxHHAcsy85aKQuulNdT881uz3zdRDHP5Q/rk813LSjWPX7HxkB4f/1vASZn5bGAJxbSB0+NpFWO7bR3JzK2Z+WBTUyexLFjMLeKcfl1P7Yc4YepGR5Gs9t21bBHnF+jfaxkU/zO1hWL6zb69nuq9zPztpr+wrCmbL8nMByoMa8FNP+8yoT4Q+DDwW1XH1yO1/vzW8PcNxV9V++bzXdekuuoVG2/NzP8qtzdQrFnfz6tPDsqKmdOv6+H9EOe0G13fXstpcfbltQTIwluBW4Hndjmmqu8N6r6TgLdGxA3AMRHxlxXH0xNlBe8a4F2ZuanqeHqktp/fmv6+oc8+37X5D26aqldsvCoijo6IBvBy4K0t4umn1ScHZcXM6df1u1XH2eJG15fXskWcfXctyzjPi4jXlU+fCFzc5ZiqvjeoyzLz+Kaq9Xcy8+yqY+qRNwLHAmPll43PqDqgHqjz57eOv++++3zXdUz1tfRuxcZW3gd8HAjgMzPE00+rT3Y7voWKebfrmplfjIj9K46z+UY3BnwUeG0fXsvpcX4FuIr+upZQrJp1dUScDdxO8d/mjX14PdWHmoaDLHqZeSlwadVx9Ni11PTzW9Pf92764fNd28Vfos9WbGwVTydt/R5fL69/v8XptexunIN8PSV1l59fVam2SbUkSZLULXUdUy1JkiR1jUm1JEmS1CGTai0aEXFFRHwnIjZExJtm6bOyRfsxEXHMtH7/HMVyt9dExJJZjrtm+j4j4kkR8c49PhlJ6pGI+J1yxohHy8dXRMR7I2JND4692723qf2DHezzhg5C2tNjtn0eVcSn3jCp1mLzO8CLgfdExFHzeN8x5U+zt5XL3T5MMRfmTNYAK5sbMvPuzLx4HseXpEpk5p+VMyfcVU5P9ukeHv4YHn/vJTN/r4cxdMMxLI7zUAfqOqWeFrHMvD8iPge8IiLeDxwA/H1mXlR2eX9EPAX4Zmb+fkRcRLHEKRHx2sx84eS+IiIoFhHYXk7TdDXFtGs3ZuZYRHyUYgnkl0fE9zJztHzfSuC9mfn68vmBwJUUcytv9EYraQCcHBHvA/YHXgJsBT4G/BxwW2a+NSL2Bq4ADgX+A3hDZm4vq7HfAo7KzBdHxEiL9852771hcoq0iHhCeYynAA8Ar6YoCn6SYhXFH2bmG9o9qYg4mOJePkSxYuwYRXHkhsy8ISJeX3b95PRjRMR7y/c8v+m6nNvOecwQS6vrspRi3YD9gfuB0zNzR7vnp+pYqdZidT/wh8DfZOZzKZLeg8rXPpeZ/x04IiKOycx3USwicnHzzZBidcE7gB8DXwaeTLEk6inArwGUN/IrgN+bTKhn8G7grzPz+cABEfGS7pymJC2YX8rM44FPAScCa4Hby7afL/8a+Kay7QXAD5haIns1cHNmvrh8/rj3znLvnW4t8N3MfB7wt8AzgZ+nuEefBKyMiPksSb4W+FSZ7G6dpd9Mx9jtuszjPGaKZfo1PQLYWbZ9lKKwowFgUq3F6kCgAZxTVkz2oaikAHyjfPw28Iuz7ONtFJPp/1sWc0/uoEiq/xLYb57xHNF03G8AvzLP90tSr32sfNwM7AU8neIvgDcAT6UoNDTf225h6t52e2Z+qmlfrd7brl8GvlluX0FRAX8MOBsYp7jfL235ztZWAN8rt7/T4vXJfc10jOnXpROtrsu3gdsj4nqK4YzbOjyGesSkWotORDyRopr8j8A7y2rExcBPyi7/rXw8iqllbB8FRsr3R9Pu/hx4YxTLdb8duIjiJts8wftM7232PaZW91rN1A1dkvrVI9Oe/yvwwfKeej5FUjnTve3hNt4L7d0//wX41XL73RT34DdSDM34jRZxzuXfgSPL7cl/D7YDy8vtyb8kznSMVsdr5zxaaXVdjga+npkvApZRDDXRADCp1mLzYeAfgPOA1wPviIivU9wkf1z2eVXZ9qPM3Fi2fQE4rWzfdQPLzC0UQz9eCXwW+AjF0vLbImKy0vK3wDsj4hZmrnxfBJwZEV8DHsjM67txspLUQ38BnBIRNwJvBu6k+MvdM8q2wykqye2+F2a497Z477FlNfdY4Kryfe+iuD/D/CrffwH8ekR8Bdi7bPsM8LaI+AjF8MHJ2No9RjvnMVMs06/LHcDvRsQ/AU8CNsxjf6qQKypKkqRaKr94eENm3lBxKFoETKolSZKkDjn8Q5IkSeqQSbUkSZLUIZNqSZIkqUMm1ZIkSVKHTKolSZKkDplUS5IkSR36/x/yJFXRRW2kAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "\n",
    "ax = plt.subplot(2,2,1)\n",
    "sns.distplot(df_train.DebtRatio, fit=stats.norm)\n",
    "ax = plt.subplot(2,2,2)\n",
    "res = stats.probplot(df_train.DebtRatio, plot=plt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.6.特征：MonthlyIncome（月收入）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "\n",
    "ax = plt.subplot(2,2,1)\n",
    "sns.distplot(df_train.MonthlyIncome, fit=stats.norm)\n",
    "ax = plt.subplot(2,2,2)\n",
    "res = stats.probplot(df_train.MonthlyIncome, plot=plt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.7.特征：NumberOfOpenCreditLinesAndLoans（未偿还的贷款或信用额度的数量）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "\n",
    "ax = plt.subplot(2,2,1)\n",
    "sns.distplot(df_train.NumberOfOpenCreditLinesAndLoans, fit=stats.norm)\n",
    "ax = plt.subplot(2,2,2)\n",
    "res = stats.probplot(df_train.NumberOfOpenCreditLinesAndLoans, plot=plt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.8.特征：NumberOfTimes90DaysLate（近两年内出现90天逾期或更坏的次数）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "\n",
    "ax = plt.subplot(2,2,1)\n",
    "sns.distplot(df_train.NumberOfTimes90DaysLate, fit=stats.norm)\n",
    "ax = plt.subplot(2,2,2)\n",
    "res = stats.probplot(df_train.NumberOfTimes90DaysLate, plot=plt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.9.特征：NumberRealEstateLoansOrLines（抵押贷款和房地产贷款数量，包括房屋净值信贷额度）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GflaIlFyS4N1kGUABnAQsC2+mPwYKsrRnKuetjmmeYBGpCK0EDRH/Dhzh7he7+/Xu/nKW40RESkYxp0VLl8tiGWsIukCsIhhA8UyGMn8B5obPFwAb8hJdFkOnSFNLsIiUM3c/tdgxiIgciNS0aKlZIVLTokFhE2DIrSX4VgYPoHjKzK4cUuY6YFE46OIy4Ot5jXIYA32CUy3Bmh1CREREpJS0tsLkyUGXh/e9r7jToqXL2hLs7h1mthA4F7g67PLw+JAyuwgGaRRUKudVS7CIiIhI6WlthQ99CHp7Ry5XqGnR0uXSHQJ3b2NghoiSsW9LsJJgERERkVKxdGn2BBgKNy1aumivGOdDZofQPMEiIiIiJSPXFt5CTYuWLqeW4FKVWjGuWvMEi0gFMLMzhnvN3e8tZCwiIrmYNSsY/DaSlpbCD4qDiCfBA32Cw+4QriRYRMpaavrJM4E+YDVwIjAeOL1IMYmIZNTaCttGXMonWBzjW98qTDxDRTsJHrJinAbGiUg5c/cvAZjZ3e7+ptR+M/tj8aISEdnXSAPizMA9WBzjqquK0woM5ZIEp1qCE5oiTUQqQtLM/pZgpp5jix2MiMhQIw2ImzUrWBij2KI9MC7VHSKmZZNFpKK8BxgHXAJMoAhTVIqIjGSkAXHFmA4tk/JoCdYUaSJSQdy93czuAGYCG4HuIockIjLISAPiijEdWibRbgnWsskiUoHM7DvAl4CvECxZ/5PiRiQiMtgFF2TeX1NTnOnQMimPJFgtwSJSWY5393cB7e5+OzCx2AGJiKS0tsKNN+67v7ERrr++eAPhhop2Ehz2CY6rT7CIVJatZvYFoMnMPgBsLnZAIiIpH/sYdHbuu79Y8wEPJ9pJcNgSHI8ZVTEjkdTsECJSEd4P7ARWErQCf2ikwmY20cx+Z2Z3mtktZlZjZteZ2Uozu7wQAYtIZbjsMti9O/NrpTIgLiXiA+OCrzEz4jFTS7CIVAR33wuMZnr5xcA33P0uM7uWYFaJuLufZmbXm9k8d18/JsGKSEVZtmz410plQFxKWbQEx1ItwYlg+/drN/Pgc9uLGZqIyJgxs9+Npry7X+Pud4WbU4D3ATeH23cCb8xwjSVmttrMVm/duvWA4hWRytDaConE8K+XyoC4lPJIgo1BLcFfv/MZfrDiuWKGJiIylp40s3eM9iAzOw1oAl4EXg537wCmDi3r7svcfYG7L5gyZcoBBSsi5S+1QtxwzEqrPzBEPQlOppJgoyoe658dYm9Pgt3dfcUMTURkLL0W+JmZPWRm9+SybLKZNQPfAf4vsBuoC19qJOJ/C0Sk+P7u74ZfIQ6CwXKlpiz7BHf3JejsGaE9XkQkwtx90WjKm1kN8Avg8+6+wczWEHSBWAXMB57Jf5QiUilaW2F7ll6o11xTmFhGI9L//Q/0CWbQ7BBdvUm1BItIWTOz48zsPDM72swasxT/MHAysNTMlgMGXGpm3wAuBm4f22hFpFy1tsKll45c5tBDCxPLaEW6JTjhA90h0luCu3oTdHarJVhEylO4YtwMYA5wBfA14O3DlXf3a4Frh5zjN8C5wNXuvnPsohWRctXaCu9/P3iWyblKbUBcSk4twbnOJ2lmU83s0fyEll0yGTRnxCw1T7DTm0jSl3T2qCVYRMrXAa8Y5+5t7n6zu2uhDRHZL0uXDixcNpxSWyAjXdYk2MwuIpxPEphrZvNGKP51BgZbjLmkOzELVotLtQR39QYtwHt6+vBs/5qIiESTVowTkaLbsCF7mW+NZkbzAsulJXghWeaTBDCzs4A9DHMzHos5J5PuxMIaVMViJBJOV28yfI3+5yIiZWZUK8aJiOTbZZdlL1PKrcCQW5/gBgbPJ3ny0ALhyOMrgHcCt2Y6ibsvA5YBLFiwIC9NtMnk4JbghA+0BAPs7u6jriaej0uJiBSdmZ2Rtvlo+ABYANxb+IhEpBKdcw7cfffIZaqqSrsVGHJLgnOZT/JzwDXu3m5hUloISac/Ca6KB32Cu/sGkuDOnj6gtmDxiIiMsdTUaGcCvcAa4ERgPHB6kWISkQqSSwIMcMMNpd0KDLklwbnMJ3kOcJaZfQI40cx+6O4fyV+YmQV9goPnqT7Be3sGukBomjQRKSfu/iUAM7vb3c9L7c9lsQwRkQN12WW5JcDxeOknwJBbEnwrsMLMZgDnA5eY2ZXu3j9ThLv3f0RnZssLkQBDqk9w2BIczhPcldYSvEfTpIlIeUqa2d8CjwPHFjsYESl/l10G116bvRzAkiVjG0u+ZB0Y5+4dBIPjVgGL3P3x9AQ4Q/mFeYsui2Qy6A6xY8sr9O54hb7E4D7Be3rUEiwiZek9wDjgEoKBce8pbjgiUu6WLcutXENDaa4Ol0lOi2W4exsDM0SUjKQ7yfX38r1f/ga3GHMuvpyu3rn9r2uuYBEpR+7eDlxd7DhEpPy1tsL73pdb2VgMvv/9sY0nnyK9bHLn1hfpW/PfzD7qBGLVtbx4+3fZuWv3wOvqDiEiZcjMflfsGESk/I0mAQa46aZo9AVOiXQSvHPdCohV8c6PfY7DL/goPe2beWzViv7XNTBORMrUk2b2jmIHISLlq6kp9wTYDH7842glwBDhJLi3p5tdz6yiatZ86hon0HL4CcQbmnjywYGpMtUdQkTK1GuBn5nZQ2Z2j2aHEJF8amqC9vbcy//oR9FLgCHCSfAzjzxAsqeT2nmvB6A6Hqduzkk8++Rq6O0CYE+PukOISPlx90XuXufurwufn1XsmEQk+i67LGjVHU0CDNFMgCHHgXGl6NknVxMb10jN9COAYJ7g2tknsXvtH4lveZr6wxaoJVhEyoqZxYCLCVqCJwAdwEPAL9xd68SLyH4bzRRo6c4+O/+xFEokW4LdnQ1PP0Ht9COIx4IqVMWM2JQ51I2fRHzzU0ysq1YSLCLl5jrgHcBzwAPA88BF4X4Rkf2W6xRo6c4+G/7wh/zHUiiRbAlu3/YqO7dvYcKRi/oXy4jHYiSSxuTZR7P32adprK3SPMEiUm4OS1+cKPSfZnZftgPNbCrwS3c/Pdy+DjgGuN3dr8x/qCISBbkug5xuxgx4+eWxiaeQItkSvOHpxwGonnokMUtfMc6ZNOtI6GyjumenVowTkXKzzcy+amYXmNmZZvYWM/s3YMtIB5lZE3Aj0BBuXwTE3f00YK6ZzRvzyEWk5FRyAgyRTYKfoH78RGziVOJhEhyPG31JZ/zMwwFIbn1eLcEiUm4uBdqBjwJXAB8GtoX7R5IA3kvQhxiCVUBTCyDdCbwxz3GKSIkyG3iMNgH++MfLJwGGiHaH2Pi/a5l15PFsJVidBFItwUlqWg7Bqmro3vwsvTPmFzVOEZF8cvc9wFf347gOAAsbDQhahFN/ynYAJw89xsyWAEsAZs2atR/RikipGbgFjN7HPx6d5ZBzFbmW4La2Ntq3bmbm3KNIuvd3h4jHgpbg7qRRO3Uuu15er+4QIiKZ7QbqwueNZPhb4O7L3H2Buy+YMmVKQYMTkfyZOXOg5Xd/uZdfAgwRTIKfeuopAGbMmUcyyT59grt6EzTOnMeuzS+wu3NPMUMVESlVaxjoAjEfeKF4oYjIWJk5E1555cDOUV2dn1hKUeS6Q6xduxaAaYfOI/nyy4STQ4SzQwRJ8IQZc9nmTueWjUWMVESkZN0KrDCzGcD5wKnFDUdExkI+EuCenvzEUooi1xK8du1aWqYdzLj6hqA7RGxoS3CS5plzAUhuf5GePs0fLyIC4O4Lw68dBIPjVgGL3H1nEcMSkTxKH/h2INzLOwGGiCbB0+cEq8QlnX36BHf1JpjQPJnGic3E2l/SghkiIhm4e5u73+zum4sdi4jkx4Emvinu+TlPqYtUErxlyxZeffVVZswOprRMJn1Qn2CAPT19jKuKM3PuEcTaX2S3kmAREREpM/H44FbfA0mAY7Eg8U09KkWkkuDUoLiBlmAf6BMcD5Pg7gTjqmPMnncUtnsr29s7Mp5LREREJIricUjmqbdnLAaJCp1MK3JJcCwWY9qswwBIOIP6BEPYElwd5/AjjsZwnlq3rmjxioiIiORbPhLgVKtvpSbAkGMSbGbXmdlKM7t8mNcnmtnvzOxOM7vFzGryG2Zg7dq1zJ07l5pxwfSW6d0h4uGqGe5QVx3n6GOOAeDpPysJFhERkegZ2t0hHwPeIqunBzry++l+1inS0teYN7PrzWyeu68fUmwx8A13v8vMrgXeDPwmn4G6O2vXruX000/v35d0J+wF0d8SDDCuOsaMaQeRrJvEc8/8OZ9hiIiIiIy5sUx2i9rv1x127oTt2wc/duzYd1/6/l274C1vgdtuy1soucwTvJB915gflAS7e/o6IlOALUNPcqBLcG7evJnt27dz3HHH9e9LuvcvAxoblATHaaipwicezIZnnxn1tURERETGQrFacsck8e3qypy4jpTUtrUN3wfDDCZNgpaW4DFtGhx7LDQ3B9vHHpvX8HNJgrOuMZ9iZqcBTe6+auhr7r4MWAawYMGCUX8rUotkHHvssazrDvalT5H2yAtt/WWfeGknSYfkpIPZ/vRadu3axfjx40d7SREREZG8KdkEOJEIktPhktfhEtq9e4c/Z13dQDLb0gInnBB8TSW0mR6TJgWj/goklyQ46xrzAGbWDHwHeFd+QhvsqaeeoqqqiqOOOop1jwcNzcmkE3YF7v8KUB2PUVsVI9l0CADr1q3jlFNOGYuwREREpAKVZt9cx3ft2Td5vSZLUtvePnymHI8HiWsqeZ01C046aSBxzZTUNjcHSXCJyyUJTq0xv4pgjfl9+heEA+F+AXze3TfkNcJQMpnkNa95DePGjQu23XEGWoJjaT+N1XEL+ghPOhgIWpGVBIuIiEg+FCIBrqKXZnbQwvb+x9DtTPsZP8Iyb+PHD05U58zJnMCmb0+YMLilsYzkkgTfyuA15i8xsyvdPX2miA8TdJNYamZLgWvd/ef5DPTTn/70oO3UPyyZk+AYZkZNw3gamg7q70ohIiIiUljOBDqyJq9D909k+JkQeqgedMR65vEgp/CRf8rQxSCV1DY3Q82YTN4VWVmTYHfvMLOFwLnA1eESm48PKXMtcO1YBDicZJgFp2aHSB8YVx0P/mOprYozYcZcnnjiiUKGJiIiIgVUqK4JtXTRwnaOy6FFNn1fFcNPxtvGpP7SWziIP3M022lhB80ZrhDs35VsZLoZ0wtT7bKVS0sw7t7GwAwRRZVIOr2JZH8SnEp+03JgqsPMuKYqRuOMw1n/1CpeffVVpk6dWvB4RUREZOzsTwIcI8Ek2kdsjc30WgOdw56zk7pBietajtsneR16hTaaSDK6gWCVtKzxWMspCS4l1933HNcsf5ZPLpoHZO4OUdXfEhyjeupcAB577DHOO++8AkcrIiJSeQo3aMypp3PU/WabaCNG5mwyQaw/Yd1BMy9xMI8zP2tC28WBDQRTclt4kUuCn3qlg/bOXvb09AEDLcBDB8ZB0BLsDTOpra3l0UcfVRIsIiIyxvY3AY7TN2x3gpGS2nF0D3vOXTQOOmoDhw7bxSD1fCcT8dwW1M0bJcDFEbkkeFN7FwBte4LRj/3dIYZMkQZBn+C9CTjuuON49NFHCxuoiEiFam2FpUth48ZgNqWrroLFi/fdf8EFcMcdsGFDMAtTIhEkUEoIoi4YCJZLi2z6Y6SBYL1UDSr9Fw7P2m92B830UFvAegf08xsdkUuCX9kZTMy8ozNMgoeZHQKgvibO89v2cPaJJ3LTTTfR3d1NbW3hfyFEREqRmV0HHAPc7u5X5uOcra2wZAl0hl0nN2wItu+/H268cfD+a9OGU6cWkFICUVpq6B5VN4PUdjV9w56znYn9pbcxmWc4csRuBttpYTeNQElOzCsRFqkkOJF0Nu8c0hKcKQkOW4dnNdezZkMbM447ht7eXh577DHNFywiApjZRUDc3U8zs+vNbJ67rz/Q8y5dOpDopnR2wrJlw6+UKmPPSPYPBBtNUtvInmHPuZdxgxLXdRyT00CwRLRSj1HRP3HREqmfxG27u+lLBj9hOzp7gYE+wfG0fxBTA+PmtDQA0N00h6qqKu6//34lwSIigYUMzPpzJ8GiSP1JsJktAZYAzJo1K+eTbtyYeb8S4PypGzIQLJeEtok24iQzni9BjDaa+o96hRk8yfEj9pvdTgt7qS9wzUdPSamMJFJJ8CvtA2tU79snOPgaNyMePm9prGHK+Foe29zF/Pnzue+++/ZZdENEpEI1AC+Hz3cQLHjUz92XAcsAFixYkHMqMWtW0NVhqFSfXxkQp48m2kbVb7aZHdTRNew5d9Owz0CwkfrNbqeFdiYVfCBYISgBlmwilQRvCrtC1FbFaBumT3BVWpOwmXHKnGYefG4HH3jDG/j2t7/N9u3baWlpKXDkIiIlZzf0z+nUCPnJgq66anCfYID6evjABwb3CS4vznh2jarfbAvbmcTOYc/YS9Wg5PU55vIwr806GKwYA8FGQ4mplJJIJcGpluBZzfWs37IbSFsxLkyCU4PiUk6Z08xtT2xi3pmvBWDlypW89a1vLVDEIiIlaw1BF4hVwHzgmXycdPHi4Gum2SHe8IbSnx2imp5R95ttZgc19A57ztRAsFTyup55WfvO7mI85TYQTAmwlJpIJcGbdnYxrjrG9Inj+pPggZbgoEx1fPBN45S5QavvtuoptLS08Ic//EFJsIjw6MY2jpg6nobaSN0G8+lWYIWZzQDOB07N14kXLx5IhnPZPyaSSdi5E7ZvH3js2DF4O9P+3buHP2dtLbS0BI/mZmg5amB70P607aYmJlVXMwk4rEBVF5HcROruv2nnXmZMrGNiXXX/vqF9gquGtAQfPqWRpvpqVm9o54ILLuDnP/85HR0dTJgwoXCBi0hJ2dPdx7u/t5KPnTmXz5x3VLHDKQp37zCzhcC5wNXuPvxn88W2d2/25HXo/h07gkQ4EzNoahpIVKdPh2OP3TehHZrU1tcXcik0ERljkUqCX2nvYsakOibW1fTvG9onuGZIEhyLGa+b08zyZ7ZwatOx9PT08Ovb7uDSv7qkcIGLSEl5dGM7iaTz2tnNxQ6lqNy9jYEZIsZeIjGQoI4mqe0afiAY9fWDk9ZDDsncIpue1E6aFPS/EJGKFqkkeNPOvZwxbwoT69Nagm3w16r4vv+ln3fsNO5a9yrLe2tJNh7Ej3/2SyXBIhXs4Rd2EDN4zaFNxQ6lfN18M9xww+Bktr19+PLx+ODkdfZseM1rhu9mkNo3blyBKiQi5SYySXBvIsmWXd1Mn1RHY1ofvv6W4FjmgXEAF518MBeeOBMzWPD8CjY+/CseeeQRTj755H3Kikj52duT4P6/bOPsow/CzFi9YQdHTZvA+HHV2Q+W/bNrF2zZEiSrhx02cr/ZlhaYMEFdDUSkoCKTBL/a0YU7zJg4jkTSqYoZfUlPawkOk+BY5ptoKkl+24Xv5mdP3MW/f+Ob/PhHN2G66YqUvWv/9Czfvns9P19yKicf2sQjG9p572sPKXZY5e3DHw4eIiIlKjJJcGqO4OmT6ni5bS8T66rZvqdnYGBcf3eIwS3BP3lw8PJFtePG0XvE2Tyy5lbuuusuDj3hNA4aX0tTQw0iUh5WPrudRNJ547zJ9CWS3PzwiwD89KGNjKuOs7c3wYLZ6gohIlLJIrNETGqO4BkTg/5fqX7BQwfGZeoOke6Q5nqajjuT8dPn8tnPfZ63XPlL3vaf97Fh+77ro7smNRQpeYmk8/OHN7KlI/hHefPOLj5y48N85KaHeamtk+XPbGVzRxeHTWngjrWbuXPdZoCKHxQnIlLpIpMEp7cEA0yqGy4JHrl7Q8yMN58wk46TFtOVjFH7wLV0bFjHu7+3knWvdABB8vutP6xnwZV/4P6/bAPg6c0dvPk/7uU7d6/vT45//djL3HD/8ySTA8lyd5/WBRU5EHt7EoP+AV357HaefGlg9q7WBzdwybKVvLAt+Mf1X+/4M5/97yd5//UPsbu7jytvX0dv0jGM/3fbOn7y0EYOGl/Lty45iZ6+JD+493lmNdczdYIGVImIVLKcukOY2XXAMcDt7n7l/pY5EJva9zJ+XFX/oLiJ/Ulw8HosTOeztQQD1MTjdFVPZOLZn6TxkR+z457v0jf1CN659gTeedbroLaRnz3RRmN9PR+64WH+/pwjuHb5X+juS/Lvd/0vW3Z1092X4ObVLwFwzzNb+esz5nLtn55lxfptvH3+DP7mrMN58qWd/ObxV5jZVMdfvW4W9TVx7v7zFtr39rDwyIM4ctp41rzQxrpNHRwzfQKvndPMS22dPPxCG+NrqzhlbjM18RirN7TRtqeH+YdMYs7kBp7ZvIv/fXUXh7Y0cPzMiXR09fLnTR3UxGMcNX0CddVxnt26m7bOHuZMbmD6xDq27OrixR17aW6oYVZzPT2JZH/r96EtDYyrirFpZxdtnT1MmziOKY21tHf2smlnF+PHVTEt7Iu9aWcXiWSS6RPrqKuOs21PN+2dvUxurKWpvprOngSvdnRRVxNnSmMtDmzb3U1PX5KDxo9jXHWMnXt7ae/sZVJ9NRPrqunuS7Jtdzc18RjNDTWYGTv29NDVm2ByYy3jqmPs6u6jfU8vE+qqmFhXTU8iyY49PcTNaGqoIW7Gzr29dPYmaK6voa4mTmdPHzv29NBYGxzTl3R27AmW225uqKEqFhyzu7uPpvoaGmqr2NuTYEdnD3XVcSbVVZNwp21PDwl3mhtqqIkH8Xfs7WNSQzXja6vo6g3ir62O0dJQi7uzfU8PPX1JpoyvpbYqOKats5dJddVMqq+mqzfJqx1d1FTFmDK+FgO27OqmsyfB1Am1NNZWsWNPD1t3d9NUX8OUxlr29ibYtHMvMTNmTKojHjM2tXfR0dXL9InjaKqvYdvubl5u30tTfQ0zm+ro7En0J4uzJzcwrjrGhu2dbN3VzazmeqZPHMemnV38ZetumutrmDe1kb09CZ56pYOeviTHzJhAU30Na1/ZyQvb9nDE1PEcOW08z27dzSMb22lpqOG1s5vp6k2wYv022vf28PrDJnNocz33PLOFRza2cdIhTZx55BQe3djObx9/hUn11Vx08sEkkkl+uOJ5Nmzv5OIFB3PqYS18955n+e3jr3Dq3Gb+7uwjuOXRl7h59UvEDJaccRjuzvfvfY6YwUXXPsBFJ83kuvueZ9GRU7h3/TYu/t5K1m3q4FPnzKOmKsbVvw8WQfubRYdz3MyJnDRrEo9ubFdXCBERwbJ95G9mFwFvd/cPmtn1wFfcff1oy6RbsGCBr169elSBfvSm1Wzc3sn//P0Z/OTBjTz8/A5ueexl/uHcI2hprCXpzhW3rmXRUQdxztFTRzxX0p0Hnt3O0dPGM74qycN/+A0P//G37NqxNdM7gJthZlRXxUk69CWD/TVVwf7uviR4MLA5Hg7Yw1PvTfjUB51y8Ha+DT1/tu2xPIZRnnd/jhnL+Cutzvk25PxDfx/Mgkf/mgYGVTEjkfRgiVULP91x6E0EB1XHjep4jK7eBEkPfufGVcfoSzrdvUliBnU1wRywe3sTJJNQXxPHjP4ytdUxqmLGFVdcwYUXXjj6apmtcfcF+/muRM7+3LNFRErFcPfsXJLgbwO/d/c7zOwSoM7d/2s/yiwBloSbR3Jg69RPBrYdwPGlolzqAapLKSqXekDp1eVQd59S7CAKxcy2AhsKeMlS+34XSqXWGyq37qp3YWS8Z+fSHaIBeDl8vgPINLlu1jLuvgxYllOoWZjZ6nJohSmXeoDqUorKpR5QXnWJokIn/JX6/a7UekPl1l31Lq5cBsbtBurC543DHJNLGRERERGRkpBLsroGeGP4fD7wwn6WEREREREpCbl0h7gVWGFmM4DzgUvM7Ep3v3yEMqfmO9Ah8tKtogSUSz1AdSlF5VIPKK+6SHaV+v2u1HpD5dZd9S6irAPjAMysCTgXuNfdN+9vGRERERGRUpBTEiwiIiIiUk40gE0qnpk1m9m5Zja52LGISMDMpprZo8WOo1DMbKKZ/c7M7jSzW8ysptgxydip9O93qfx+Ry4JNrPrzGylmV2evXTpCb/xK9K2I1efTL+8UawH9HfjuQ14HXCPmU2Jal1g8I0lqvUwsyoz22hmy8PH8VGtixyQrzMw61AlWAx8w93fBGwG3lzkeAqign+3K/L7naYkfr8jlQSHK9PF3f00YK6ZzSt2TKMRJlw3EsyrHOX6DP3lvYRo1gPgBODT7n4V8D/AWUS3LhDeWCL8swXB9+Sn7r7Q3RcC84huXWQ/mNlZwB6C+0tFcPdr3P2ucHMKsKWY8RRCxO9TB6QSv98ppfT7ncvsEKVkIXBz+PxOgmnZhl2euQQlgPcCvw63FxLB+rj7NWmbU4D3Af8RbkemHgDu/icAMzuDoDW4mQh+T2CfG8tCIloPgtll3mpmi4AngW6iWxfJwsy+T7CKaMofgUXAOwlmHipLmert7l82s9OAJndfVaTQCmkhFf67XWHfb8JuH1dQIr/fUUuCc1m9rmS5eweAmaV2Rbo+qV9egnmho1wPI/jnpA1wIliXDDeWKP9sPQyc4+6bzOwmgtb574evRa0ukoW7/3X6tpl9AbjG3dvT7pVlZ2i9IRifAHwHeFfhIyqKKN+nDlgFfr8BPkcJ/X5HqjsE5bcyXWTrk/bL+3+JcD0APPAJ4Ang9USzLv03lnA7yt+TJ9x9U/h8NcEa81Gti4zeOcAnzGw5cKKZ/bDI8RRE+I/sL4DPu/uGYsdTIFG+Tx2QCv1+Q4n9fkftB67cVqaLZH0y/PJGsh4AZvZZM3t/uDkJ+CrRrMugGwvwNqJZD4Afmdl8M4sDFwKfILp1kVFy9zPS+oM/5u4fKXZMBfJhgpbQpeGA0PcWO6ACiOzfjjyoxO93yf1+R2qeYDObAKwA7iZcmc7ddxY3qtEzs+XuvjCq9TGzjwP/Cjwe7vov4NNErB7QP1jxZqAWWAt8HriXCNYlJUyE304Ef7YAzOw44CeAAb8BvkZE6yIiw4vq30ApH5FKgqH8VqYrl/qUSz2gfOpSLvWA8qqLiAzQ77YUU+SSYBERERGRAxW1PsEiIiIiIgdMSbCIiIiIVBwlwRFnZjeY2b+Gz79oZl8cxXGzD+Caj4ZLXf7CzKr34xzLM5wvtUzutBGOu9DMJuVw/qzlwmsdPIqwR83MjjKz+8zsQTP71GhjMbMTzeyDYxmjiESLmf1NeM/YG359Z3j/X1iAa59oZidm2P8fB3DO5QcQ0v5eM+d6FCM+KQwlweXho2Y2rsDX/GS41OVugum58nG+heFjpMERFxJMZZZNruXG2rcIZtI4A/hbMztkNAe7+2PufsNYBCYi0eTu/xlOMfVyeM+8pYCXPzF8DI3pUwWMIR9OpDzqIQdASXB5WAssTm2kWgPM7IPhY42Z/c7Mfh22SH4sLPo1M7vfzL4Zlq83s1+a2b1m9t208y03s38zs/9Jv2i40loj0GNmU8NrPGBmnw9fnxG2gq4ws6tGU6G0FtRVZnZFuO93wJuBn5nZv4f7jjezh8J6XDZCuSPM7J6w/u/PfFUwszlmdndYj8+McI3lZvaZsNxvLLBPzMApBKOeu4FHgJPNbKGZfdPMbsn2voRlv5i2nem6+9RtmFhEpLydG96/HzOzaZnu6WZWa2Y/NbM/mVmrBfO+73OfH+bYrxAszPM5M7s7/cI2+NO9cWb2s/AedFt4rkYz+3349+C/RlMpM5tsZn8MY7w/dV/M8Ldun2uE5a4a8r7kVI9hYsn0vtSF9bw3vK9HbTXeiqUkuDx8F9hnCc409cB7gBOAvyJIzABud/c3AMdY8LHQEmCtu58BTDezE8JypwIr3f28tHN+h2Bi81eBPxLMr/tzd389cKGZtQAzCW405xMs3jCS74Q3uF+E228BfuXup4bXwd3PB34PXOLu/xCWm0qwat3bgQ+NUO5q4EvA6cBnzYZdr/HfgC8AbwDOM7OjM10jtCesbyMwPVPMwHhgT/i8E5gQPr8Y+Iy7L83yvmQy9LqZ6pYpFhEpb4eH9+9fESw3nume/tFw35nAeoJ7G+x7n9/nWHf/PMGCQl9197NHiGMJ8Li7vxH4b+A4gnvVdwg+OZxtZlNHUa8lBPezhUDHCOWGu8ag92UU9RgulqHv6TFAMtz3XwT3ZokAJcHlYTPwNLBwyP7UcpSvuvtuYAOQIFiEAODB8OsjwGHAkcA7w/+E5xIksRD8wv9qyLk/CVwLPOvBPHtHAh8Pj20AZgB9BEnwDwmSwZGkukO8J9z+EXCCmd0FTBzhOCNIAr8JjPTf95EEieKdQJzhu0ocDTwY1mk1cNQI17gx/LoRqBkm5g4GbogNDNzAf+rufxkh3pEMvW6muuX6/olI+bgp/Jp+bxh6Tz+GgXv/KoJ7Hux7nx/u70EujgIeCp/fADwM9AIfAVqBZgb+PuViFvBU+PyxDK+nzjXcNYa+Lwci0/vyCLDWzO4EziNo8JAIUBJcPr4JnAn8MzAl3PfmLMe8Jvx6AkFr4TPAf4T/bV9OcMOAoN9vJt8HPmzB8rbPAJ8Lj/0qsINgFbmvENyURjsh9ULgqrAOn7GBwXd7CVq2U75A0LLxTwQJIMOUewb4YBjffwI9w1x3HXBq2Jr6WuDPw13D3fcMOTZTzA8CC82slmCJzDVh2eHe06wyXDdT3TLFIiLlLdO9Yeg9/SmCVl/Cr6nkcug9abi/B/331hE+UXua4P4Jwd+kjxAsE/xL4P9kiDOb54Djw+epv1s97Pu3brhrZLpeLvXIJNP7Mh+4393fBDQRfConEaAkuEy4+6PAnwiWmf2kmX0P2J7lsHeb2f3A8+6+BvgBcL6Z3Qt8DHgxyzXbCLpCvIsg8f3H8HxvJugmcRvwvTCmTjMbqSUh1R1iuZmdSXDTuxF4APidu/eG5W4CfmBBH906go+37gCuA6ptYIDg0HKfC7dXAXPSEsnbzWx1+DiNINH9cnjd37v70yNcY6hMMX8qvPYK4Fvu/tII78HQWHKVqW7DvX8iUjky3dN/CBwb7ptH0FKb67EAdwEXhff64ZK9HxCMf1hO8M//j8LjPk/wNwNG17L8A+DtZnYPwRL3kPlv3WiukUs9hotl6PvyAsHA5weAaQSfIkoEaMU4ERERiQQLBgovd/flRQ5FyoCSYBERERGpOOoOISIiIiIVR0mwiIiIiFQcJcEiIiIiUnGUBIuIiIhIxVESLCIiIiIVR0mwiIiIiFSc/w9NQWUFEDtM6gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "\n",
    "ax = plt.subplot(2,2,1)\n",
    "sns.distplot(df_train.NumberRealEstateLoansOrLines, fit=stats.norm)\n",
    "ax = plt.subplot(2,2,2)\n",
    "res = stats.probplot(df_train.NumberRealEstateLoansOrLines, plot=plt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.10.特征：NumberOfTime60-89DaysPastDueNotWorse（近两年内出现60-89天逾期的次数）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "\n",
    "ax = plt.subplot(2,2,1)\n",
    "sns.distplot(df_train['NumberOfTime60-89DaysPastDueNotWorse'], fit=stats.norm)\n",
    "ax = plt.subplot(2,2,2)\n",
    "res = stats.probplot(df_train['NumberOfTime60-89DaysPastDueNotWorse'], plot=plt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.11.特征：NumberOfDependents（家庭中不包括自身的家属人数（配偶、子女等））"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,6))\n",
    "\n",
    "ax = plt.subplot(2,2,1)\n",
    "sns.distplot(df_train.NumberOfDependents, fit=stats.norm)\n",
    "ax = plt.subplot(2,2,2)\n",
    "res = stats.probplot(df_train.NumberOfDependents, plot=plt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4.特征分箱"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Best_KS_box(object):\n",
    "    def __init__(self, data, feature, target):\n",
    "        # 对A进行分箱\n",
    "        result = self.univeral_df(data, feature, target, 'total', 'good', 'bad')\n",
    "        cut_bin = self.cut_main_fun(data_df=result, feature=feature, rate=0.05, total_name='total',\n",
    "                                    good_name='good', bad_name='bad')\n",
    "        data[feature + '_bin'] = pd.cut(data[feature], cut_bin, right=True)\n",
    "        self.result = data.groupby([feature + '_bin', target], observed=True)[target].count().unstack().reset_index()\n",
    "\n",
    "    def univeral_df(self, data, feature, target, total_name, good_name, bad_name):\n",
    "        \"\"\"\n",
    "        :param data: 原数据，类型为DataFrame\n",
    "        :param feature: 特征名，类型为str\n",
    "        :param target: 目标变量，二分类，0为好样本，1为坏样本\n",
    "        :param total_name: 自定义column，汇总标签\n",
    "        :param good_name: 自定义column，好样本标签\n",
    "        :param bad_name: 自定义column，坏样本标签\n",
    "        :return: 类型为DataFrame，columns=['feature', 'total', 'bad', 'good']\n",
    "        \"\"\"\n",
    "        data = data[[feature, target]]  # 截取特征与目标\n",
    "        result = data.groupby(feature)[target].agg(['count', 'sum'])  # 分组统计\n",
    "        result = result.sort_index()  # 排序index\n",
    "        result.columns = [total_name, bad_name]  # 重塑column\n",
    "        result = result.fillna(0)  # 填充缺失值\n",
    "        result[good_name] = result[total_name] - result[bad_name]  # 计算出好样本的数量\n",
    "        result = result.reset_index()  # 重塑index\n",
    "        return result\n",
    "\n",
    "    def get_max_ks(self, data_df, start, end, rate, total_name, good_name, bad_name):\n",
    "        \"\"\"\n",
    "        寻找能满足的分箱占比的最大的KS对应的值\n",
    "        \"\"\"\n",
    "        total_all = data_df[total_name].sum()\n",
    "        limit = rate * total_all\n",
    "        data_cut = data_df.loc[start:end, :]  # data_cut统计的范围包含end\n",
    "        data_cut['bad_rate_cum'] = (data_cut[bad_name] / data_cut[bad_name].sum()).cumsum()\n",
    "        data_cut['good_rate_cum'] = (data_cut[good_name] / data_cut[good_name].sum()).cumsum()\n",
    "        data_cut['total_cum'] = data_cut[total_name].cumsum()\n",
    "        data_cut['total_other_cum'] = total_all - data_cut[total_name]\n",
    "        data_cut['KS'] = (data_cut['bad_rate_cum'] - data_cut['good_rate_cum']).abs()\n",
    "        try:\n",
    "            cut = data_cut[(data_cut['total_cum'] >= limit) & (data_cut['total_other_cum'] >= limit)]['KS'].idxmax()\n",
    "            return cut\n",
    "        except:\n",
    "            return np.nan\n",
    "\n",
    "    def verify_cut(self, data_df, cut_list, total_name, good_name, bad_name):\n",
    "        \"\"\"\n",
    "        判断是否能继续分箱下去,返回True(继续进行切割)或False(不继续切割)。具体判断条件如下：\n",
    "        1 是否存在某箱对应的类别全为0或1\n",
    "        2 现有的分箱能否保证bad rate递增或递减\n",
    "        3 woe的单调性和bad rate的单调性相反(这个条件感觉太严格了)\n",
    "        \"\"\"\n",
    "        bad_all = data_df[bad_name].sum()\n",
    "        good_all = data_df[good_name].sum()\n",
    "        cut_bad = np.array([data_df.loc[x[0]:x[1], bad_name].sum() for x in cut_list])\n",
    "        cut_good = np.array([data_df.loc[x[0]:x[1], good_name].sum() for x in cut_list])\n",
    "        cond1 = (0 not in cut_bad) and (0 not in cut_good)\n",
    "        cut_bad_rate = cut_bad / bad_all\n",
    "        cut_good_rate = cut_good / good_all\n",
    "        cut_woe = np.log(cut_bad_rate / cut_good_rate)\n",
    "        cond2 = sorted(cut_woe, reverse=False) == list(cut_woe) and sorted(cut_bad_rate, reverse=True) == list(\n",
    "            cut_bad_rate)\n",
    "        cond3 = sorted(cut_woe, reverse=True) == list(cut_woe) and sorted(cut_bad_rate, reverse=False) == list(\n",
    "            cut_bad_rate)\n",
    "        cond4 = sorted(cut_bad_rate, reverse=False) == list(cut_bad_rate) or sorted(cut_bad_rate, reverse=True) == list(\n",
    "            cut_bad_rate)\n",
    "        return cond1 and cond4\n",
    "\n",
    "    def cut_fun(self, data_df, start, end, rate, total_name, good_name, bad_name):\n",
    "        \"\"\"\n",
    "        对从start到end这一段数据进行下一步切分，并返回新的割点对\n",
    "        \"\"\"\n",
    "        cut = self.get_max_ks(data_df, start, end, rate, total_name, good_name, bad_name)\n",
    "        if cut:\n",
    "            return [(start, cut), (cut + 1, end)]\n",
    "        else:\n",
    "            return [(start, end)]\n",
    "\n",
    "    def cut_main_fun(self, data_df, feature, rate, total_name, good_name, bad_name, bins=None, null_value=False, missing_value=[]):\n",
    "        \"\"\"\n",
    "        bins: 分箱数。默认为None,即不限定分箱数目。若为int,则为指定的分箱数\n",
    "        null_value: 布尔型。字段中填充的缺失值是否需要单独划分为一箱。若为True,则单独划分为一箱。\n",
    "        missing_value:若null_value为True,则missing_value中的数据即为缺失值,每个缺失值会单独成一箱\n",
    "        \"\"\"\n",
    "        if null_value and missing_value:  # 如果null_value和missing_value为真，则向下执行\n",
    "            data_df = data_df[~data_df[feature].isin(missing_value)]  # 对缺失值取反，即取出非空\n",
    "        start = data_df.index.min()  # index最小值\n",
    "        end = data_df.index.max()  # index最大值\n",
    "        cut_list = [(start, end)]  # 真正有效的割点集合\n",
    "        tt = cut_list.copy()  # 数据深复制\n",
    "        for cut_seg in tt:\n",
    "            cut_bin = self.cut_fun(data_df, cut_seg[0], cut_seg[1], rate, total_name, good_name, bad_name)\n",
    "            if len(cut_bin) > 1:\n",
    "                temp = cut_list.copy()\n",
    "                index_seg = temp.index(cut_seg)\n",
    "                temp[index_seg] = cut_bin[0]\n",
    "                temp.insert(index_seg + 1, cut_bin[1])\n",
    "                if self.verify_cut(data_df, temp, total_name, good_name, bad_name):\n",
    "                    cut_list = temp\n",
    "                    tt.extend(cut_bin)\n",
    "            if bins and len(cut_list) > bins:  # 判断是否达到限定的分箱数\n",
    "                break\n",
    "        # 将割点对转化为对应的数值\n",
    "        # cut_list中保留的割点对中的数据为data_df中的inde，这里想要获得真正的feature的割点数据则需要依据data_df的index找到对应的feature字段的值\n",
    "        cut_list = sorted([-np.inf] + [data_df.loc[item[0], feature] for item in cut_list] + [np.inf] + missing_value)\n",
    "        return cut_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
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       "      <th>SeriousDlqin2yrs</th>\n",
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       "SeriousDlqin2yrs NumberOfDependents_bin      0     1\n",
       "0                           (-inf, 0.0]  81807  5095\n",
       "1                            (0.0, inf]  54422  4752"
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150000 entries, 0 to 149999\n",
      "Data columns (total 21 columns):\n",
      " #   Column                                    Non-Null Count   Dtype   \n",
      "---  ------                                    --------------   -----   \n",
      " 0   Unnamed: 0                                150000 non-null  int64   \n",
      " 1   SeriousDlqin2yrs                          150000 non-null  int64   \n",
      " 2   RevolvingUtilizationOfUnsecuredLines      150000 non-null  float64 \n",
      " 3   age                                       150000 non-null  int64   \n",
      " 4   NumberOfTime30-59DaysPastDueNotWorse      150000 non-null  int64   \n",
      " 5   DebtRatio                                 150000 non-null  float64 \n",
      " 6   MonthlyIncome                             120269 non-null  float64 \n",
      " 7   NumberOfOpenCreditLinesAndLoans           150000 non-null  int64   \n",
      " 8   NumberOfTimes90DaysLate                   150000 non-null  int64   \n",
      " 9   NumberRealEstateLoansOrLines              150000 non-null  int64   \n",
      " 10  NumberOfTime60-89DaysPastDueNotWorse      150000 non-null  int64   \n",
      " 11  NumberOfDependents                        146076 non-null  float64 \n",
      " 12  RevolvingUtilizationOfUnsecuredLines_bin  150000 non-null  category\n",
      " 13  age_bin                                   150000 non-null  category\n",
      " 14  NumberOfTime30-59DaysPastDueNotWorse_bin  150000 non-null  category\n",
      " 15  DebtRatio_bin                             150000 non-null  category\n",
      " 16  MonthlyIncome_bin                         120269 non-null  category\n",
      " 17  NumberOfOpenCreditLinesAndLoans_bin       150000 non-null  category\n",
      " 18  NumberRealEstateLoansOrLines_bin          150000 non-null  category\n",
      " 19  NumberOfTime60-89DaysPastDueNotWorse_bin  150000 non-null  category\n",
      " 20  NumberOfDependents_bin                    146076 non-null  category\n",
      "dtypes: category(9), float64(4), int64(8)\n",
      "memory usage: 15.0 MB\n"
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   "source": [
    "df_train.info()"
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  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
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     "data": {
      "text/plain": [
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     "execution_count": 83,
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   "source": [
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   "execution_count": 70,
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       "1    (25.0, 40.0]\n",
       "2    (25.0, 40.0]\n",
       "3    (25.0, 40.0]\n",
       "4    (40.0, 50.0]\n",
       "Name: age, dtype: object"
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     "metadata": {},
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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